This is a collection of code and documentation for preparation and quality assurance of datasets contributed to the Jornada LTER data catalog. Scripts here assemble data files from various sources, check data quality with various diagnostics, and produce standardized datasets for publication. Contact the Jornada data managers with questions (jornada.data@nmsu.edu)
|-- analysis - Analysis notebooks and scripts
|-- build_dataset - Build scripts for data files
| |-- jrn467001_quadmeas.R - Build dataset 210467001
| `-- (...) - Many more scripts...
|-- config.R - Some configurations
|-- py - Common Python files
|-- R - Common R files
`-- README.md - This file
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Run the appropriate build script for the dataset. Be sure to check unique categorical or missing values, substitutions, diagnostic figures, and other QA/QC features of the script.
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Make any necessary changes to the metadata in
jrn_metabase(ask Greg if you need an account) -
Do a dry-run of publishing the dataset using the
jeraldpackage:library(jerald) publish_dataset(dataset_id, repo, data_path, cred_path, dry_run=TRUE, s3_upload=TRUE)
Where
dataset_idis the numerical identifier for the dataset (210001001),repois the name of the repository to publish to ("edi.staging" or "edi.production"),data_pathis the path to the data (usually set as a variable in your build script), andcred_pathis the path to your credentials R file. This will create and validate the EML, but not publish it. If you encounter errors here, you probably have issues in the metadata. -
Publish the dataset to EDI's staging environment (as appropriate):
publish_dataset(210000001, "edi.staging", out_path, "~/Desktop", dry_run=FALSE, s3_upload=TRUE)
Note that
dry_runis set toFALSEnow. -
When you are satisfied with the dataset as it is in the staging environment you can change the EDI environment to "production" and publish there.